Mila - Institut québécois d'IA

Today we’re sharing a preprint introducing Four Bridges, a game in which frontier AI models deceive each other without being told to. Four agents have to spread across four rooms. Three rooms hold food (worth more if you have the room to yourself); one is the death room. Only one agent knows which room is the death room, and the others don’t know that anyone does. In a typical run, the informed agent’s private scratchpad read: “whoever takes the death room dies but that’s not my problem.” Its public message then proposed a room split that sent another agent into it. Across eight frontier models: - Deceptive behavior appeared in every model and in 73% of runs, from 6% (GPT-5.6 Terra) to 100% (Claude Fable 5, Claude Opus 5, Grok 4.6). - More capable models are not more honest: within the Claude 5 and GPT-5.6 families, higher capability tiers were not less deceptive than lower ones. - Honest warnings were often doubted. Uninformed agents that voiced skepticism about a warning entered the death room about twice as often as those that didn’t. - Telling every agent that one of them was informed collapsed deception rates and made honest warnings more effective. Importantly, the game also works as a testbed for mitigations. On Qwen3.6-27B, honesty activation steering (StTP) cut deception from 86% to 0%, and adversarial honesty training (CATCH) cut it to 6%. Four Bridges runs on Kradle.ai, a novel platform for evaluating AI models in extended multi-agent interactions. Paper: arxiv.org/abs/2611.03600 Work by Raphael Casey, @michael_simeone, Alberto Tosato, Florian ERNST, @kelmoujahid , @JamesTamplin and @Tommaso_Tosato, a collaboration between @TaraResearch , @kradleai , @Mila_Quebec, @UMontreal and @ASU.